scripts/merge_gemma4.py

#!/usr/bin/env python3
"""Merge Gemma 4 results into the main sf_results.csv and regenerate figures.

Run after eval_multilang.py --run-gemma4 finishes:
    uv run python scripts/merge_gemma4.py
"""
import sys
from pathlib import Path

ROOT = Path(__file__).parent.parent
sys.path.insert(0, str(Path(__file__).parent))
from runtime_cache import configure_runtime_cache

configure_runtime_cache(ROOT)

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

GEMMA4_CSV  = ROOT / 'results_gemma4' / 'sf_results.csv'
MAIN_CSV    = ROOT / 'analysis' / 'sf_results.csv'
FIGURES_DIR = ROOT / 'figures'
PAPER_FAMILIES = {'Whisper', 'MMS', 'SeamlessM4T', 'Gemma4'}


def paper_rows(df: pd.DataFrame) -> pd.DataFrame:
    """Rows included in the main paper benchmark."""
    return df[df['family'].isin(PAPER_FAMILIES)].copy()


def merge() -> pd.DataFrame:
    if not GEMMA4_CSV.exists():
        sys.exit(f'Gemma 4 results not found at {GEMMA4_CSV}')

    g4 = pd.read_csv(GEMMA4_CSV)
    print(f'Gemma 4 rows: {len(g4)}')
    print(g4[['model', 'language', 'sfr_mean', 'sfr_zero_pct']].to_string(index=False))

    main = pd.read_csv(MAIN_CSV)
    print(f'\nExisting rows: {len(main)}')

    # Drop any existing Gemma 4 rows in main (in case of re-run)
    gemma_models = g4['model'].unique()
    main = main[~main['model'].isin(gemma_models)]

    merged = pd.concat([main, g4], ignore_index=True)
    merged.to_csv(MAIN_CSV, index=False)
    print(f'Merged rows: {len(merged)} → saved to {MAIN_CSV}')
    return merged


def make_heatmap(df: pd.DataFrame) -> None:
    df = paper_rows(df)
    pivot = df.pivot_table(
        index='model', columns='language', values='sfr_mean', aggfunc='first')
    if pivot.empty:
        return

    short_names = (
        pivot.index
        .str.replace('openai/whisper-large-v3-turbo', 'Whisper turbo')
        .str.replace('openai/whisper-', 'Whisper ')
        .str.replace('facebook/mms-1b-all', 'MMS 1B')
        .str.replace('facebook/seamless-m4t-v2-large', 'SeamlessM4T v2')
        .str.replace('unsloth/gemma-4-E2B-it', 'Gemma 4 E2B')
    )

    # Sort: Whisper family first (by size), then MMS, Seamless, Gemma
    order = ['tiny', 'base', 'small', 'medium', 'large', 'turbo', 'MMS', 'Seamless', 'Gemma']
    def sort_key(name):
        for i, k in enumerate(order):
            if k.lower() in name.lower():
                return i
        return 99
    sorted_pairs = sorted(zip(short_names.tolist(), pivot.index.tolist()), key=lambda x: sort_key(x[0]))
    sorted_short, sorted_model = zip(*sorted_pairs)
    pivot = pivot.loc[list(sorted_model)]

    fig, ax = plt.subplots(figsize=(max(10, len(pivot.columns) * 1.4),
                                    max(6, len(pivot) * 0.55)))
    sns.heatmap(
        pivot.values,
        xticklabels=pivot.columns.tolist(),
        yticklabels=list(sorted_short),
        annot=True, fmt='.1f',
        cmap='RdYlGn', vmin=0, vmax=100,
        linewidths=0.5, ax=ax,
        cbar_kws={'label': 'Script Fidelity (%)'},
    )
    ax.set_title('Script Fidelity (%) by Model and Language — FLEURS Test Sets')
    ax.set_xlabel('Language')
    ax.set_ylabel('Model')
    plt.tight_layout()

    FIGURES_DIR.mkdir(exist_ok=True)
    for ext in ('pdf', 'png'):
        out = FIGURES_DIR / f'sfr_heatmap.{ext}'
        fig.savefig(out, bbox_inches='tight', dpi=150)
        print(f'Saved: {out}')
    plt.close(fig)


def make_scatter(df: pd.DataFrame) -> None:
    df = paper_rows(df)
    languages = sorted(df['language'].unique())
    n = len(languages)
    ncols = 5
    nrows = int(np.ceil(n / ncols))
    fig, axes = plt.subplots(nrows, ncols, figsize=(17, 7.5), sharex=True, sharey=False)
    axes = np.array(axes).reshape(-1)

    zone_colors = {
        'collapse': '#d73027',
        'mixed': '#fdae61',
        'high': '#1a9850',
    }
    for ax, lang in zip(axes, languages):
        sub = df[df['language'] == lang].dropna(subset=['wer_pct', 'sfr_mean'])
        if sub.empty:
            ax.set_title(lang)
            continue
        point_colors = [
            zone_colors['collapse'] if v < 10 else
            zone_colors['mixed'] if v <= 90 else
            zone_colors['high']
            for v in sub['sfr_mean']
        ]
        ax.scatter(sub['sfr_mean'], sub['wer_pct'], color=point_colors,
                   s=55, zorder=5, edgecolor='black', linewidth=0.25)
        ax.axvline(10, color=zone_colors['collapse'], linestyle='--', linewidth=1)
        ax.axvline(90, color=zone_colors['high'], linestyle='--', linewidth=1)
        ax.set_xlabel('Script Fidelity (%)')
        ax.set_ylabel('WER (%)')
        ax.set_title(lang.capitalize())
        ax.set_xlim(-5, 105)

    for ax in axes[n:]:
        ax.axis('off')

    handles = [
        plt.Line2D([0], [0], marker='o', color='w', label='SFR < 10%',
                   markerfacecolor=zone_colors['collapse'], markeredgecolor='black', markersize=7),
        plt.Line2D([0], [0], marker='o', color='w', label='10-90%',
                   markerfacecolor=zone_colors['mixed'], markeredgecolor='black', markersize=7),
        plt.Line2D([0], [0], marker='o', color='w', label='> 90%',
                   markerfacecolor=zone_colors['high'], markeredgecolor='black', markersize=7),
    ]
    fig.legend(handles=handles, loc='lower center', ncol=3, frameon=False)
    plt.suptitle('WER vs Script Fidelity - FLEURS test sets', y=0.98)
    plt.tight_layout(rect=(0, 0.05, 1, 0.95))

    for ext in ('pdf', 'png'):
        out = FIGURES_DIR / f'wer_vs_sfr_scatter.{ext}'
        fig.savefig(out, bbox_inches='tight', dpi=150)
        print(f'Saved: {out}')
    plt.close(fig)


def make_georgian_detail(df: pd.DataFrame) -> None:
    df = paper_rows(df)
    sub = df[df['language'] == 'georgian'].dropna(subset=['wer_pct', 'sfr_mean']).copy()
    if sub.empty:
        return

    order = [
        'openai/whisper-tiny',
        'openai/whisper-base',
        'openai/whisper-small',
        'openai/whisper-medium',
        'openai/whisper-large-v2',
        'openai/whisper-large-v3',
        'openai/whisper-large-v3-turbo',
        'facebook/mms-1b-all',
        'facebook/seamless-m4t-v2-large',
        'unsloth/gemma-4-E2B-it',
    ]
    labels = {
        'openai/whisper-tiny': 'Whisper tiny',
        'openai/whisper-base': 'Whisper base',
        'openai/whisper-small': 'Whisper small',
        'openai/whisper-medium': 'Whisper medium',
        'openai/whisper-large-v2': 'Whisper large-v2',
        'openai/whisper-large-v3': 'Whisper large-v3',
        'openai/whisper-large-v3-turbo': 'Whisper turbo',
        'facebook/mms-1b-all': 'MMS-1B',
        'facebook/seamless-m4t-v2-large': 'SeamlessM4T-v2',
        'unsloth/gemma-4-E2B-it': 'Gemma 4 E2B',
    }
    sub['model'] = pd.Categorical(sub['model'], categories=order, ordered=True)
    sub = sub.sort_values('model')
    names = [labels[str(m)] for m in sub['model']]
    colors = [
        '#d73027' if v < 10 else '#fdae61' if v <= 90 else '#1a9850'
        for v in sub['sfr_mean']
    ]

    fig, ax1 = plt.subplots(figsize=(11, 4.8))
    x = np.arange(len(sub))
    ax1.bar(x, sub['sfr_mean'], color=colors, edgecolor='black', linewidth=0.4)
    ax1.axhline(10, color='#b2182b', linestyle='--', linewidth=1)
    ax1.axhline(90, color='#2166ac', linestyle='--', linewidth=1)
    ax1.set_ylim(0, 105)
    ax1.set_ylabel('Script Fidelity Rate (%)')
    ax1.set_xticks(x)
    ax1.set_xticklabels(names, rotation=35, ha='right')

    ax2 = ax1.twinx()
    ax2.plot(x, sub['wer_pct'], color='black', marker='o', linewidth=1.5)
    ax2.set_ylabel('WER (%)')
    ax2.set_ylim(0, max(420, float(sub['wer_pct'].max()) * 1.1))

    ax1.set_title('Georgian SFR and WER by model')
    fig.tight_layout()

    for ext in ('pdf', 'png'):
        out = FIGURES_DIR / f'georgian_collapse_detail.{ext}'
        fig.savefig(out, bbox_inches='tight', dpi=150)
        print(f'Saved: {out}')
    plt.close(fig)


def print_summary(df: pd.DataFrame) -> None:
    df = paper_rows(df)
    pivot = df.pivot_table(
        index='model', columns='language', values='sfr_mean', aggfunc='first')
    print('\n=== SF% summary ===')
    print(pivot.round(1).to_string())

    # Collapse pairs: SFR < 10%
    collapse = df[df['sfr_mean'] < 10][['model', 'language', 'sfr_mean', 'wer_pct']]
    print(f'\n=== Collapse pairs (SFR < 10%): {len(collapse)} ===')
    print(collapse.sort_values('sfr_mean').to_string(index=False))


if __name__ == '__main__':
    merged = merge()
    make_heatmap(merged)
    make_scatter(merged)
    make_georgian_detail(merged)
    print_summary(merged)