| """Gera graficos do benchmark 1K (811 testes)."""
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| import json
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| import os
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| import matplotlib
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| matplotlib.use("Agg")
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| import matplotlib.pyplot as plt
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| import numpy as np
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| plt.rcParams.update({
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| "figure.facecolor": "#0d1117",
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| "axes.facecolor": "#161b22",
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| "axes.edgecolor": "#30363d",
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| "axes.labelcolor": "#c9d1d9",
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| "xtick.color": "#8b949e",
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| "ytick.color": "#8b949e",
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| "text.color": "#c9d1d9",
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| "grid.color": "#21262d",
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| "grid.linestyle": "--",
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| "grid.alpha": 0.6,
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| "font.family": "sans-serif",
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| "font.size": 11,
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| })
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| OUTPUT_DIR = "training_plots"
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| def load_results(path="benchmark_1k_results.json"):
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| with open(path, "r", encoding="utf-8") as f:
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| return json.load(f)
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| def plot_accuracy_bars(data, save_dir):
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| fig, axes = plt.subplots(1, 3, figsize=(16, 6))
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| ax = axes[0]
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| modalities = ["Texto\n(401)", "Imagem\n(250)", "Audio\n(160)", "COMPOSTO\n(811)"]
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| accs = [
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| data["text"]["_total"]["acc"],
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| data["image"]["both_acc"],
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| data["audio"]["acc"],
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| data["composite_score"],
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| ]
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| colors = ["#58a6ff", "#7ee787", "#d2a8ff", "#ffa657"]
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| bars = ax.bar(modalities, accs, color=colors, width=0.6, edgecolor="#30363d", linewidth=1.2)
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| for bar, acc in zip(bars, accs):
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| ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1,
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| f"{acc:.1f}%", ha="center", va="bottom", fontsize=12, fontweight="bold",
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| color="#c9d1d9")
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| ax.set_ylim(0, 110)
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| ax.set_ylabel("Acuracia (%)")
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| ax.set_title("Por Modalidade", fontsize=13, fontweight="bold", pad=10)
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| ax.grid(axis="y", alpha=0.3)
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| ax = axes[1]
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| cats = []
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| cat_accs = []
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| for cat, r in data["text"].items():
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| if cat != "_total":
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| label = cat.replace("(fora treino)", "\n(fora treino)")
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| cats.append(label)
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| cat_accs.append(r["acc"])
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| bar_colors = ["#58a6ff"] * len(cats)
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| bars2 = ax.barh(cats, cat_accs, color=bar_colors, height=0.5, edgecolor="#30363d", linewidth=1)
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| for bar, acc in zip(bars2, cat_accs):
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| ax.text(bar.get_width() + 1, bar.get_y() + bar.get_height()/2,
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| f"{acc:.0f}%", ha="left", va="center", fontsize=11, fontweight="bold",
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| color="#c9d1d9")
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| ax.set_xlim(0, 115)
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| ax.set_xlabel("Acuracia (%)")
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| ax.set_title("Texto por Categoria", fontsize=13, fontweight="bold", pad=10)
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| ax.grid(axis="x", alpha=0.3)
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| ax = axes[2]
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| labels = ["Forma", "Cor", "Forma+Cor", "Audio"]
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| vals = [
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| data["image"]["shape_acc"],
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| data["image"]["color_acc"],
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| data["image"]["both_acc"],
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| data["audio"]["acc"],
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| ]
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| cols = ["#7ee787", "#7ee787", "#7ee787", "#d2a8ff"]
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| bars3 = ax.bar(labels, vals, color=cols, width=0.5, edgecolor="#30363d", linewidth=1.2)
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| for bar, acc in zip(bars3, vals):
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| ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1,
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| f"{acc:.1f}%", ha="center", va="bottom", fontsize=12, fontweight="bold",
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| color="#c9d1d9")
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| ax.set_ylim(0, 110)
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| ax.set_ylabel("Acuracia (%)")
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| ax.set_title("Imagem & Audio", fontsize=13, fontweight="bold", pad=10)
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| ax.grid(axis="y", alpha=0.3)
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| fig.suptitle(
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| f"Benchmark 1K | Score: {data['composite_score']:.1f}% | "
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| f"{data['total_tests']} testes (texto + img + audio)",
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| fontsize=14, fontweight="bold", y=0.98, color="#58a6ff"
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| )
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| plt.tight_layout(rect=[0, 0, 1, 0.94])
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| path = os.path.join(save_dir, "benchmark.png")
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| fig.savefig(path, dpi=150, bbox_inches="tight")
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| plt.close(fig)
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| print(f" Salvo: {path}")
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| def plot_radar(data, save_dir):
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| cats_text = {k: v for k, v in data["text"].items() if k != "_total"}
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| categories = list(cats_text.keys()) + ["Imagem", "Audio"]
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| values = [v["acc"] for v in cats_text.values()] + [data["image"]["both_acc"], data["audio"]["acc"]]
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| N = len(categories)
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| angles = np.linspace(0, 2 * np.pi, N, endpoint=False).tolist()
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| values_closed = values + [values[0]]
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| angles += [angles[0]]
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| fig, ax = plt.subplots(figsize=(8, 8), subplot_kw=dict(polar=True))
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| ax.set_facecolor("#161b22")
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| fig.patch.set_facecolor("#0d1117")
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| ax.plot(angles, values_closed, "o-", linewidth=2, color="#58a6ff", alpha=0.9)
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| ax.fill(angles, values_closed, color="#58a6ff", alpha=0.15)
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| short_names = []
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| for c in categories:
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| c2 = c.replace("(fora treino)", "").strip()
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| if len(c2) > 14:
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| c2 = c2[:13] + "."
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| short_names.append(c2)
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| ax.set_xticks(angles[:-1])
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| ax.set_xticklabels(short_names, fontsize=9, color="#c9d1d9")
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| ax.set_ylim(0, 105)
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| ax.set_yticks([20, 40, 60, 80, 100])
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| ax.set_yticklabels(["20%", "40%", "60%", "80%", "100%"], fontsize=8, color="#8b949e")
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| ax.spines["polar"].set_color("#30363d")
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| ax.grid(color="#21262d", alpha=0.6)
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| for angle, val in zip(angles[:-1], values):
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| ax.text(angle, val + 5, f"{val:.0f}%", ha="center", va="bottom",
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| fontsize=10, fontweight="bold", color="#ffa657")
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| ax.set_title(f"Benchmark 1K - Score: {data['composite_score']:.1f}%",
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| fontsize=14, fontweight="bold", pad=20, color="#c9d1d9")
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| path = os.path.join(save_dir, "benchmark_radar.png")
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| fig.savefig(path, dpi=150, bbox_inches="tight")
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| plt.close(fig)
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| print(f" Salvo: {path}")
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| def main():
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| os.makedirs(OUTPUT_DIR, exist_ok=True)
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| data = load_results()
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| print(f" Score: {data['composite_score']}% | {data['total_tests']} testes")
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| plot_accuracy_bars(data, OUTPUT_DIR)
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| plot_radar(data, OUTPUT_DIR)
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| print(" Done!")
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| if __name__ == "__main__":
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| main()
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|
