forked from UNN/2026-rff_mp
100 lines
3.8 KiB
Python
100 lines
3.8 KiB
Python
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"""plot_results.py - графики для эксперимента с лабиринтами."""
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import csv
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import os
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import matplotlib.pyplot as plt
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import numpy as np
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CSV = "docs/data/results.csv"
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PLOTS = "docs/data/plots"
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os.makedirs(PLOTS, exist_ok=True)
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def load_means():
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"""Возвращает dict[(maze, strategy)] = (time_ms, visited, path_len, cost)."""
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out = {}
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with open(CSV, encoding="utf-8") as f:
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rows = list(csv.reader(f))
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start = next(i for i, r in enumerate(rows) if r and r[0] == "--- СРЕДНИЕ ---") + 2
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for r in rows[start:]:
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if not r:
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continue
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maze, strat, t, vis, plen, cost = r
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out[(maze, strat)] = (float(t), int(vis), int(plen), int(cost))
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return out
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MAZES = ["small_10x10", "medium_51x51", "large_101x101",
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"empty_30x30", "nopath_15x15",
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"weighted_31x31", "weighted_choice"]
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STRATEGIES = ["BFS", "DFS", "A*", "Dijkstra"]
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COLORS = {"BFS": "#3498db", "DFS": "#e67e22", "A*": "#2ecc71", "Dijkstra": "#9b59b6"}
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def grouped_bar(means, idx, ylabel, title, fname, log=True):
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x = np.arange(len(MAZES))
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w = 0.2
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fig, ax = plt.subplots(figsize=(11, 5))
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for i, s in enumerate(STRATEGIES):
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vals = [means[(m, s)][idx] for m in MAZES]
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bars = ax.bar(x + (i - 1.5) * w, vals, w, label=s, color=COLORS[s], alpha=0.9)
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for b, v in zip(bars, vals):
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ax.text(b.get_x() + b.get_width() / 2, b.get_height(),
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f"{v:g}", ha="center", va="bottom", fontsize=7, rotation=0)
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ax.set_xticks(x)
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ax.set_xticklabels(MAZES, rotation=20, ha="right")
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ax.set_ylabel(ylabel)
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ax.set_title(title)
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if log:
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ax.set_yscale("log")
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ax.legend()
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ax.grid(axis="y", linestyle="--", alpha=0.4)
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plt.tight_layout()
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p = os.path.join(PLOTS, fname)
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plt.savefig(p, dpi=130)
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plt.close()
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print("saved:", p)
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def weighted_choice_chart(means):
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"""Отдельный график для weighted_choice: путь vs стоимость."""
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strategies = STRATEGIES
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lengths = [means[("weighted_choice", s)][2] for s in strategies]
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costs = [means[("weighted_choice", s)][3] for s in strategies]
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x = np.arange(len(strategies))
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w = 0.35
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fig, ax = plt.subplots(figsize=(7.5, 4.5))
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b1 = ax.bar(x - w/2, lengths, w, label="длина пути (клеток)",
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color="#3498db", alpha=0.9)
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b2 = ax.bar(x + w/2, costs, w, label="стоимость пути (сумма весов)",
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color="#e74c3c", alpha=0.9)
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for bars in (b1, b2):
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for bar in bars:
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ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height(),
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f"{bar.get_height():.0f}", ha="center", va="bottom", fontsize=9)
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ax.set_xticks(x); ax.set_xticklabels(strategies)
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ax.set_title("weighted_choice: BFS/DFS режут через болото,\n"
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"Dijkstra/A* находят более дешёвый обход")
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ax.set_ylabel("значение")
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ax.legend()
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ax.grid(axis="y", linestyle="--", alpha=0.4)
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plt.tight_layout()
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p = os.path.join(PLOTS, "weighted_choice_compare.png")
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plt.savefig(p, dpi=130)
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plt.close()
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print("saved:", p)
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def main():
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means = load_means()
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grouped_bar(means, 0, "Время, мс (среднее по 7 запускам, лог. шкала)",
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"Время поиска пути", "time_compare.png", log=True)
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grouped_bar(means, 1, "Число посещённых клеток (лог. шкала)",
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"Сколько клеток посетил алгоритм", "visited_compare.png", log=True)
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grouped_bar(means, 2, "Длина пути (клеток)",
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"Длина найденного пути", "path_compare.png", log=False)
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weighted_choice_chart(means)
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if __name__ == "__main__":
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main()
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