141 lines
4.4 KiB
Python
141 lines
4.4 KiB
Python
#!/usr/bin/env python3
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"""Generate plots from GA history for the course work report."""
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import json
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from pathlib import Path
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import matplotlib
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import matplotlib.pyplot as plt
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matplotlib.rcParams.update(
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{
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"font.family": "DejaVu Sans",
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"axes.grid": True,
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"grid.alpha": 0.3,
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}
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)
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MODEL_DISPLAY = {
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"whisper-large-v3": "Whisper large-v3",
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"whisper-medium": "Whisper medium",
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"faster-whisper-large-v3": "Faster-Whisper\nlarge-v3",
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"gigaam-ctc": "GigaAM-CTC",
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"gigaam-rnnt": "GigaAM-RNN-T",
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"pyannote-3.1": "pyannote 3.1",
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"pyannote-community-1": "pyannote\nCommunity-1",
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"sortformer": "Sortformer",
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}
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def main():
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history_path = Path(__file__).parent / "history.json"
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data = json.loads(history_path.read_text())
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history = data["history"]
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all_configs = data["all_configs"]
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img_dir = Path(__file__).parent.parent / "report" / "img"
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img_dir.mkdir(parents=True, exist_ok=True)
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plot_convergence(history, img_dir)
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plot_wer_der_scatter(all_configs, img_dir)
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plot_model_frequency(all_configs, img_dir)
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def plot_convergence(history: list[dict], img_dir: Path):
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gens = [h["generation"] for h in history]
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best = [-h["best_fitness"] for h in history]
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mean = [-h["mean_fitness"] for h in history]
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fig, ax = plt.subplots(figsize=(7, 4.5))
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ax.plot(gens, best, "b-o", markersize=4, linewidth=1.5, label="Лучшая особь")
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ax.plot(
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gens, mean, "r--s", markersize=3, linewidth=1.2, label="Среднее по популяции"
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)
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ax.set_xlabel("Поколение", fontsize=12)
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ax.set_ylabel("Значение целевой функции\n(взвешенная ошибка, меньше — лучше)", fontsize=11)
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ax.legend(fontsize=11)
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fig.tight_layout()
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fig.savefig(img_dir / "convergence.png", dpi=150)
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plt.close(fig)
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print(f"Saved {img_dir / 'convergence.png'}")
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def plot_wer_der_scatter(all_configs: list[dict], img_dir: Path):
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wers = [c["wer"] for c in all_configs]
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ders = [c["der"] for c in all_configs]
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fits = [c["fitness"] for c in all_configs]
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fig, ax = plt.subplots(figsize=(7, 5.5))
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sc = ax.scatter(
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wers,
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ders,
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c=fits,
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cmap="RdYlGn",
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alpha=0.7,
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edgecolors="gray",
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linewidth=0.5,
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s=40,
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)
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best = max(all_configs, key=lambda c: c["fitness"])
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ax.scatter(
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[best["wer"]],
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[best["der"]],
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c="blue",
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s=160,
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marker="*",
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zorder=5,
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label=f'Лучшая ({best["wer"]:.1f}%, {best["der"]:.1f}%)',
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)
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ax.set_xlabel("WER, %", fontsize=12)
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ax.set_ylabel("DER, %", fontsize=12)
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ax.legend(fontsize=11)
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cbar = fig.colorbar(sc, ax=ax)
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cbar.set_label("Фитнес", fontsize=11)
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fig.tight_layout()
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fig.savefig(img_dir / "wer_der_scatter.png", dpi=150)
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plt.close(fig)
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print(f"Saved {img_dir / 'wer_der_scatter.png'}")
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def plot_model_frequency(all_configs: list[dict], img_dir: Path):
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top_n = min(20, len(all_configs))
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top = sorted(all_configs, key=lambda c: c["fitness"], reverse=True)[:top_n]
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t_counts: dict[str, int] = {}
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d_counts: dict[str, int] = {}
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for c in top:
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tm = c["config"]["transcription_model"]
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dm = c["config"]["diarization_model"]
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t_counts[tm] = t_counts.get(tm, 0) + 1
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d_counts[dm] = d_counts.get(dm, 0) + 1
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4.5))
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t_names = sorted(t_counts.keys(), key=lambda n: t_counts[n], reverse=True)
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t_labels = [MODEL_DISPLAY.get(n, n) for n in t_names]
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t_vals = [t_counts[n] for n in t_names]
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ax1.barh(t_labels, t_vals, color="steelblue")
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ax1.set_xlabel(f"Количество в топ-{top_n}", fontsize=11)
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ax1.set_title("Модели транскрибации", fontsize=12)
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ax1.invert_yaxis()
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d_names = sorted(d_counts.keys(), key=lambda n: d_counts[n], reverse=True)
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d_labels = [MODEL_DISPLAY.get(n, n) for n in d_names]
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d_vals = [d_counts[n] for n in d_names]
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ax2.barh(d_labels, d_vals, color="coral")
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ax2.set_xlabel(f"Количество в топ-{top_n}", fontsize=11)
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ax2.set_title("Модели диаризации", fontsize=12)
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ax2.invert_yaxis()
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fig.tight_layout()
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fig.savefig(img_dir / "model_frequency.png", dpi=150)
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plt.close(fig)
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print(f"Saved {img_dir / 'model_frequency.png'}")
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if __name__ == "__main__":
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main()
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