га

ga/generate_plots.py

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